In medical imaging, it is often desirable to perform 3D imaging over a period of time to provide what is often referred to as 4D imaging. In 4D imaging, it is usually the case that imaging is performed at different patient positions (often referred to as “couch positions”) at different times. In such cases, the raw imaging data is a set of 3D images having time stamps, and the problem arises of how best to use this raw data to provide 4D imaging results. In particular, patient motion can cause considerable difficulty for 4D reconstruction.
An important special case of patient motion is breathing. For example, four dimensional (4D) computed tomography (CT) imaging is an important tool in radiation oncology. It enables tighter margins to be used during treatment planning and enhances accuracy during treatment delivery for patients with tumor motion affected by respiration. Since respiration is basically periodic, respiratory displacement or phase values can be associated with each 3D image, to facilitate reconstruction. If breathing is the dominant patient motion, then images at or near the same point in the respiratory cycle should have comparable feature locations, thereby facilitating 4D reconstruction.
Some conventional 4D reconstruction approaches are based on this observation. More specifically, 3D images can be sorted into a predefined number of bins. The most common method to acquire a 4D CT scan of a patient is to use the CT scanner in cine-mode. The time stamps of the reconstructed CT images and the measured respiratory signal of the patient are retrospectively matched. The reconstructed images are then sorted either by the phase (phase based sorting) or the displacement (displacement based sorting) of the respiratory signal into image bins, which are then stacked to create a three dimensional (3D) image of the patient for each image bin. A 4D dataset is then reconstructed by viewing the 3D images in sequence for each image bin.
More specifically, conventional procedures generate a 4D CT data set by assembling CT images using the nearest-neighbor (NN) approach, i.e. the image whose phase or displacement is numerically closest to the desired image bin value is added to that bin. Because of mismatches in the phase or displacement between adjacent couch positions, artifacts in the images will be created. These artifacts appear as discontinuities at the interface between adjacent bed positions in the 4D CT images and cause systematic errors in patient contouring and dose calculations. The prevalence of artifacts is high. In one published report, at least one artifact appeared in retrospectively phase sorted 4D CT images for 45 out of 50 patients.
It would be an advance in the art to provide improved 4D image reconstruction of 3D images of a freely breathing patient.